非ガウスノイズ分布に対する効率的な確率的衝突検出
Efficient Probabilistic Collision Detection for Non-Gaussian Noise Distributions
深度センサ由来の非ガウスな位置不確かさを持つ物体間の衝突確率を、従来より10倍厳しい上界で3倍高速に計算する手法を提案し、7自由度アームの運動計画に応用した。
著者: Jae Sung Park, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present an efficient algorithm to compute tight upper bounds of collision probability between two objects with positional uncertainties, whose error distributions are represented with non-Gaussian forms. Our approach can handle noisy datasets from depth sensors, whose distributions may correspond to Truncated Gaussian, Weighted Samples, or Truncated Gaussian Mixture Model. We derive tight probability bounds for convex shapes and extend them to non-convex shapes using hierarchical representations. We highlight the benefits of our approach over prior probabilistic collision detection algorithms in terms of tighter bounds ($10$x) and improved running time ($3$x). Moreover, we use our tight bounds to design an efficient and accurate motion planning algorithm for a 7-DOF robot arm operating in tight scenarios with sensor and motion uncertainties.
